
%pip install plotly
import matplotlib.pyplot as plt
import plotly.express as px
import numpy as np
import pandas as pd
from pandas.plotting import table
from pandas.io.json import json_normalize
from datetime import datetime

%matplotlib inline
%config InlineBackend.figure_format = 'svg' 
plt.style.use('fivethirtyeight')

Requirement already satisfied: plotly in /Users/menghsuanlee/opt/anaconda3/lib/python3.9/site-packages (5.11.0)
Requirement already satisfied: tenacity>=6.2.0 in /Users/menghsuanlee/opt/anaconda3/lib/python3.9/site-packages (from plotly) (8.1.0)
Note: you may need to restart the kernel to use updated packages.


# Set Params
num_tokens = str(1000)
api_key = 'Enter your API key here'


from requests import Request, Session
from requests.exceptions import ConnectionError, Timeout, TooManyRedirects
import json

url = 'https://pro-api.coinmarketcap.com/v1/cryptocurrency/listings/latest?aux=cmc_rank,date_added'
parameters = {
  'start':'1',
  'limit':num_tokens,
  'convert':'USD'
}
headers = {
  'Accepts': 'application/json',
  'X-CMC_PRO_API_KEY': api_key,
}

session = Session()
session.headers.update(headers)

try:
  response = session.get(url, params=parameters)
  data = json.loads(response.text)
  print(data)
except (ConnectionError, Timeout, TooManyRedirects) as e:
  print(e)


# Transform json to pandas dataframe
df = json_normalize(data['data'])

/var/folders/x7/dln2v9vs69gdf84nt96l91b40000gn/T/ipykernel_7733/2869745003.py:2: FutureWarning: pandas.io.json.json_normalize is deprecated, use pandas.json_normalize instead
  df = json_normalize(data['data'])


# Rename columns and convert data_added from date-time to date
col_name ={}
for i in df.columns:
    new = i.split('.')[-1:][0]
    col_name[i] = new
df.rename(columns=col_name, inplace=True)
df['date_added_n'] = pd.to_datetime(df['date_added'].str[:10], format='%Y-%m-%d')
df = df.sort_values('market_cap', ascending=False)


def market_cap_perc(topn, outofn=-1):    
    """
    :param:
    topn: int, >0 and  <= 100, the number of top coins want to check
    outofn: int > 0 and <= 100, the first N coins be considered in total market share; default value set to all coins 
    """
    # Error handling
    if topn > 100 or outofn >100 or topn <1 or outofn < -1 or outofn == 0:
        return 'This dataset contains only top 100 currencies, please try a number from 1 to 100'
    
    TOP_CAP_TITLE = 'Top '+str(topn)+' Market Capitalization'

    # Selecting the first N cryptos
    df10 = df.head(topn).set_index('symbol')
    
    if outofn == -1:
        TOP_CAP_YLABEL = '% of Total Cap'
        ax = df10['market_cap_dominance'].plot.bar(fontsize=10, color='#b56576')
    else:
        TOP_CAP_YLABEL = '% of Top '+str(outofn)+' Total Cap'
        # Calculating market_cap_perc, total market cap include only top 100 cryptos
        df10['market_cap_perc'] = df10['market_cap']*100/df['market_cap'][:outofn].sum()
        ax = df10['market_cap_perc'].plot.bar(fontsize=10, color='#355070')   

    # Annotating
    ax.set_title(TOP_CAP_TITLE,fontsize=15)
    ax.set_ylabel(TOP_CAP_YLABEL, fontsize=12)
    ax.set_xlabel('')
    for p in ax.patches:
        ax.annotate(str(np.round(p.get_height(), decimals=1)), (p.get_x() * 1.05, p.get_height() * 1.05), fontsize=8)
      
    
    # Tree map
    color_continuous_scale=["#355070","#355070", "#6d597a", "#b56576", "#e56b6f", "#eaac8b"]
    df10 = df10.reset_index()
    fig = px.treemap(df10, path=[px.Constant('Market Cap'), 'symbol'],
                     values=df10.market_cap,
                     color='symbol',                
                     color_discrete_sequence=color_continuous_scale,
                     hover_name='symbol'
                    )

    fig.update_layout(margin = dict(t=50, l=25, r=25, b=25))
    
    # Show plot
    ax
    fig.show()


market_cap_perc(15, 50)


def price_change(t, topn):
    """
    :param:
    t: str, time interval, options include: '1h', '24h', '7d', '30d', '60d', '90d'
    topn: int, < 0 and >= 100, top N volitile coins to show
    """

    # Check if t is valid
    t = str(t)
    valid_t = ['1h', '24h', '7d', '30d', '60d', '90d']
    if t not in valid_t:
        return 'Time interval not valid, shold be: 1h, 24h, 7d, 30d, 60d, 90d '
    
    # Check topn validity
    if topn < 1 or topn > 100:
        return 'This dataset contains only top 100 currencies, please try a number from 1 to 100'
    
    
    col = 'percent_change_'+t
    dftop = df.sort_values(col, ascending=False).head(topn).set_index('symbol')
    dftail = df.sort_values(col, ascending=False).tail(topn).set_index('symbol')
    
    # Making the subplot and the figure for two side by side plots
    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 6))
    
    # Plotting with pandas the barchart for the top gainer
    ax = dftop[col].plot.bar(ax=axes[0], color='#b56576',fontsize=10)
    for p in ax.patches:
        ax.annotate(str(np.round(p.get_height(), decimals=1)), (p.get_x() * 1.05, p.get_height() * 1.05), fontsize=8)
    
    # Setting the figure's main title to the text passed as parameter
    fig.suptitle('Top'+str(topn)+' Volitile Coins in '+t, fontsize=15)
    
    # Setting the ylabel to '% change'
    ax.set_ylabel('% change', fontsize=12)
    ax.set_xlabel('')
    
    # Same as above, but for the top loser
    ax = dftail[col].plot.bar(ax=axes[1], color='#355070', fontsize=10)
    for p in ax.patches:
        ax.annotate(str(np.round(p.get_height(), decimals=1)), (p.get_x() * 1.05, p.get_height() * 1.05), fontsize=8)
    
    return fig, ax


price_change('24h', 10 )

(<Figure size 720x432 with 2 Axes>, <AxesSubplot:xlabel='symbol'>)


def newbies(num_days):
    """
    :param:
    days: int, >=0, the number of recent days that wants to check
    """
    
    # Error handling
    if num_days < 0:
        return 'Number of days should be >= 0.'
    
    pd.set_option('display.float_format', lambda x: '%.3f' % x)
    
    # filter newly added coins within N days
    df2 = df[df['date_added_n'] > datetime.now() - pd.to_timedelta(str(num_days)+"day")]
    
    
    if df2['id'].count() < 1:
        return 'No new coin added in '+str(num_days)+' days.'
    
    df2 = df2.set_index('symbol')
    ax = (df2['market_cap']/1000000).plot.bar(color='#355070')
    ax.set_ylabel('USD(million)',fontsize=12)
    ax.set_xlabel('')
    ax.set_title('Market Cap: Newbies in Recent '+str(num_days)+' Days', fontsize=15)
    for p in ax.patches:
        ax.annotate(str(np.round(p.get_height(), decimals=1)), (p.get_x() * 1.05, p.get_height() * 1.02), fontsize=10)

    pre = df2[['name', 'date_added_n', 'price', 'volume_24h']]
    return ax, pre


newbies(90)

(<AxesSubplot:title={'center':'Market Cap: Newbies in Recent 90 Days'}, ylabel='USD(million)'>,
                    name date_added_n  price   volume_24h
 symbol                                                  
 APT               Aptos   2022-10-19  3.733 54684797.672
 TMG           T-mac DAO   2022-11-04  0.248    78287.362
 HOOK    Hooked Protocol   2022-12-01  1.420 24090613.320
 HFT            Hashflow   2022-10-31  0.325 11408754.117
 ACX     Across Protocol   2022-11-09  0.044    56872.206)

Exploring Crypto Market - Data Visualization¶

0. Import packages¶

1. Get data from Coinmarketcap¶

1.1 From CMC API¶

1.2 (Alternative) Read csv from local¶

2. Preprocessing¶

3. Top coins and their market shares¶

Example¶

4. Price changes %¶

Example¶

5. Newbies¶

Example¶